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---
icon: traffic-light-slow
description: >-
This dataset provides insights into web traffic patterns for various U.S.
government agencies and domains.
---
# Government Traffic
> **Data Notice**: This dataset provides academic research access with a 6-month data lag.
> For real-time data access, please visit [sov.ai](https://sov.ai) to subscribe.
> For market insights and additional subscription options, check out our newsletter at [blog.sov.ai](https://blog.sov.ai).
```python
from datasets import load_dataset
df_agencies = load_dataset("sovai/government/traffic/agencies", split="train").to_pandas().set_index(["date"])
```
`Tutorials` are the best documentation — [<mark style="color:blue;">`Government Traffic Analysis Tutorial`</mark>](https://colab.research.google.com/github/sovai-research/sovai-public/blob/main/notebooks/datasets/Government%20Internet.ipynb)
## Description
This dataset provides web traffic data for U.S. government agencies and domains, offering insights into public engagement with government websites.
It enables analysis of traffic trends, inter-agency comparisons, and patterns of citizen interaction with government online resources.
## Data Access
```python
import sovai as sov
sov.token_auth(token="your_token_here")
# Agency-level traffic data
df_agencies = sov.data("government/traffic/agencies")
# Domain-level traffic data
df_domains = sov.data("government/traffic/domains")
```
<figure><img src="https://raw.githubusercontent.com/sovai-research/sovai-documentation/main/.gitbook/assets/government_traffic_1 (2).png" alt=""><figcaption></figcaption></figure>
### Dataset Contents
1. **Agency Traffic (df\_agencies)**
* Provides traffic data aggregated at the agency level.
* Allows for high-level analysis of government agency website usage.
2. **Domain Traffic (df\_domains)**
* Offers more granular data on traffic to specific government domains.
* Enables analysis of individual website performance within agencies.
### Analysis Capabilities
* Time series analysis of traffic patterns
* Correlation analysis between different domains or agencies
* Calculation of statistical measures like coefficient of variation
* Filtering for specific types of domains (e.g., embassies)
### Example Analyses
1. Plotting agency-level traffic:
```python
df_agencies.plot()
```
2. Analyzing embassy website traffic:
```python
df_embassy = df_domains.loc[:, df_domains.columns.str.contains('embassy', case=False)]
df_embassy.plot()
```
3. Correlation analysis:
```python
df_embassy.corr()
```
4. Advanced statistics (e.g., coefficient of variation):
```python
cv = df_embassy.std().div(df_embassy.mean()).sort_values()
```
This dataset is valuable for understanding government web presence, analyzing public engagement with government resources, and identifying trends in how citizens interact with government websites.
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